Recent publications explore AI biases in detecting objects and people in the environment. However, there is no research tackling how AI examines nature. This case study presents a pioneering exploration into the AI attitudes (ecocentric, anthropocentric and antipathetic) toward nature. Experiments with a Large Language Model (LLM) and an image captioning algorithm demonstrate the presence of anthropocentric biases in AI. Moreover, to delve deeper into these biases and Human-Nature-AI interaction, we conducted a real-life experiment in which participants underwent an immersive de-anthropocentric experience in a forest and subsequently engaged with ChatGPT to co-create narratives. By creating fictional AI chatbot characters with ecocentric attributes, emotions and views, we successfully amplified ecocentric exchanges. We encountered some difficulties, mainly that participants deviated from narrative co-creation to short dialogues and questions and answers, possibly due to the novelty of interacting with LLMs. To solve this problem, we recommend providing preliminary guidelines on interacting with LLMs and allowing participants to get familiar with the technology. We plan to repeat this experiment in various countries and forests to expand our corpus of ecocentric materials.

翻译：近期出版物探讨了AI在检测环境中的物体和人类时存在的偏见。然而，尚无研究涉及AI如何审视自然。本案例研究首次探索了AI对自然的态度（生态中心主义、人类中心主义和冷漠态度）。通过大语言模型（LLM）和图像描述算法的实验，证明了AI中存在人类中心主义偏见。此外，为深入探究这些偏见及人-自然-AI互动，我们设计了一项真实环境实验：参与者在森林中经历沉浸式去人类中心化体验后，与ChatGPT共同创作叙事。通过创建具有生态中心属性、情感和观点的虚构AI聊天角色，我们成功强化了生态中心主义对话。实验过程中遇到的主要困难是参与者偏离叙事共创，转向简短对话及问答形式，这可能源于与LLM互动的新颖性。为解决此问题，我们建议提供与LLM互动的初步指南，并让参与者预先熟悉相关技术。未来计划在不同国家和森林中重复该实验，以扩展生态中心主义素材语料库。